A Comparative Study of Ego-Motion Estimation Algorithms for Teleoperated Robotic Endoscopes

Abstract. Colorectal cancer is one of the leading causes of cancer-related mortality in the world, although it can be efficiently treated if detected early. Colonoscopy is the most-commonly adopted visual screen-ing procedure of the colon by means of a flexible tiny endoscopic camera. In an effort to promote early screening and to facilitate mastering the endoscope motion by the physician, teleoperable robotic endoscopes and wireless capsules are being developed. In order to enable precise and fast closed-loop control for these devices, the accurate 3-D position and orien-tation of the camera must be known. Estimating the camera ego-motion by processing the endoscopic video provides a viable solution since it does not require the adoption of external magnetic trackers during the screen-ing procedure that can occupy the scope’s operation channel. Further-more, and compared to SLAM or registration approaches, ego-motion estimation algorithms do not require to deal with the highly deformable (and thus highly-uncertain) global 3D map of the colon. This paper provides researchers in the medical imaging computing com-munity with an in-depth comparison of several state-of-the-art ego-motion estimation methods that can localize the camera with respect to an initial video frame. Four optical-flow (OF) algorithms are analyzed and their performance is examined when used in two state-of-the-art (supervised and unsupervised) 6 degrees of freedom (DoF) ego-motion estimation al-gorithms. The ability for each of these methods to precisely localize the camera after a long trajectory have been examined. To the best of our knowledge, this is the first work that compares vision-based localization algorithms in a endoscopic scenario.